{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas 快捷方便处理日期数据\n",
    "* 将多种日期格式转换成统一的格式对象\n",
    "    * pd.to_datetime 能将字符串、列表、series变成日期形式\n",
    "    * Timestamp 表示日期的对象形式\n",
    "    * DatetimeIndex 表示日期的对象列表形式\n",
    "    * DatetimeIndex 是Timestamp的列表形式\n",
    "    * pd.to_datetime 对单个字符串处理会得到Timestamp\n",
    "    * pd.to_datetime 对日期列表处理会得到DatetimeIndex\n",
    "* 与时间相关的概念\n",
    "   * 日期时间：具有时区支持的特定日期和时间\n",
    "   * 时间增量：绝对持续时间\n",
    "   * 时间跨度：由时间点及其关联的频率定义的时间跨度\n",
    "   * 日期偏移量：尊重日历算术的相对持续时间"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 导入模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import requests_html\n",
    "from requests_html import HTMLSession\n",
    "import datetime\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 爬取 pandas Time series 所提供的表格\n",
    "（如果不想在GitHub整个下载就用爬取的放方式）\n",
    "\n",
    "### 用read_html抓取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>date.utc</th>\n",
       "      <th>location</th>\n",
       "      <th>parameter</th>\n",
       "      <th>value</th>\n",
       "      <th>unit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-06-21 00:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>20.0</td>\n",
       "      <td>µg/m³</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-06-20 23:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>21.8</td>\n",
       "      <td>µg/m³</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-06-20 22:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>26.5</td>\n",
       "      <td>µg/m³</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-06-20 21:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>24.9</td>\n",
       "      <td>µg/m³</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-06-20 20:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>21.4</td>\n",
       "      <td>µg/m³</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2063</th>\n",
       "      <td>NaN</td>\n",
       "      <td>London</td>\n",
       "      <td>GB</td>\n",
       "      <td>2019-05-07 06:00:00+00:00</td>\n",
       "      <td>London Westminster</td>\n",
       "      <td>no2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>µg/m³</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2064</th>\n",
       "      <td>NaN</td>\n",
       "      <td>London</td>\n",
       "      <td>GB</td>\n",
       "      <td>2019-05-07 04:00:00+00:00</td>\n",
       "      <td>London Westminster</td>\n",
       "      <td>no2</td>\n",
       "      <td>16.0</td>\n",
       "      <td>µg/m³</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2065</th>\n",
       "      <td>NaN</td>\n",
       "      <td>London</td>\n",
       "      <td>GB</td>\n",
       "      <td>2019-05-07 03:00:00+00:00</td>\n",
       "      <td>London Westminster</td>\n",
       "      <td>no2</td>\n",
       "      <td>19.0</td>\n",
       "      <td>µg/m³</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2066</th>\n",
       "      <td>NaN</td>\n",
       "      <td>London</td>\n",
       "      <td>GB</td>\n",
       "      <td>2019-05-07 02:00:00+00:00</td>\n",
       "      <td>London Westminster</td>\n",
       "      <td>no2</td>\n",
       "      <td>19.0</td>\n",
       "      <td>µg/m³</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2067</th>\n",
       "      <td>NaN</td>\n",
       "      <td>London</td>\n",
       "      <td>GB</td>\n",
       "      <td>2019-05-07 01:00:00+00:00</td>\n",
       "      <td>London Westminster</td>\n",
       "      <td>no2</td>\n",
       "      <td>23.0</td>\n",
       "      <td>µg/m³</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2068 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0    city country                   date.utc  \\\n",
       "0            NaN   Paris      FR  2019-06-21 00:00:00+00:00   \n",
       "1            NaN   Paris      FR  2019-06-20 23:00:00+00:00   \n",
       "2            NaN   Paris      FR  2019-06-20 22:00:00+00:00   \n",
       "3            NaN   Paris      FR  2019-06-20 21:00:00+00:00   \n",
       "4            NaN   Paris      FR  2019-06-20 20:00:00+00:00   \n",
       "...          ...     ...     ...                        ...   \n",
       "2063         NaN  London      GB  2019-05-07 06:00:00+00:00   \n",
       "2064         NaN  London      GB  2019-05-07 04:00:00+00:00   \n",
       "2065         NaN  London      GB  2019-05-07 03:00:00+00:00   \n",
       "2066         NaN  London      GB  2019-05-07 02:00:00+00:00   \n",
       "2067         NaN  London      GB  2019-05-07 01:00:00+00:00   \n",
       "\n",
       "                location parameter  value   unit  \n",
       "0                FR04014       no2   20.0  µg/m³  \n",
       "1                FR04014       no2   21.8  µg/m³  \n",
       "2                FR04014       no2   26.5  µg/m³  \n",
       "3                FR04014       no2   24.9  µg/m³  \n",
       "4                FR04014       no2   21.4  µg/m³  \n",
       "...                  ...       ...    ...    ...  \n",
       "2063  London Westminster       no2   26.0  µg/m³  \n",
       "2064  London Westminster       no2   16.0  µg/m³  \n",
       "2065  London Westminster       no2   19.0  µg/m³  \n",
       "2066  London Westminster       no2   19.0  µg/m³  \n",
       "2067  London Westminster       no2   23.0  µg/m³  \n",
       "\n",
       "[2068 rows x 8 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = \"https://github.com/pandas-dev/pandas/blob/master/doc/data/air_quality_no2_long.csv\"\n",
    "session = HTMLSession()\n",
    "r = session.get( url )\n",
    "\n",
    "# read_html()可快速抓取网页上的表格，[0]代表网页上的第一个表格\n",
    "df_空气指数 = pd.read_html(url)[0] \n",
    "df_空气指数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 用 requests+lxml 库爬取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import lxml\n",
    "import pandas as pd\n",
    "from lxml import etree\n",
    "res = requests.get('https://github.com/pandas-dev/pandas/blob/master/doc/data/air_quality_no2_long.csv')\n",
    "res_elements = etree.HTML(res.text)\n",
    "table = res_elements.xpath('/html/body/div[4]/div/main/div[2]/div/div[3]/div[2]/div[2]/table')\n",
    "table = etree.tostring(table[0], encoding='utf-8').decode()\n",
    "\n",
    "df_空气指数 = pd.read_html(table, encoding='utf-8', header=0)[0]\n",
    "results = list(df_空气指数.T.to_dict().values())  # 转换成列表嵌套字典的格式\n",
    "df_空气指数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将行标签重命名\n",
    "df_空气指数 = df_空气指数.rename(columns={\"date.utc\": \"datetime\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看此时的datetime类型\n",
    "type(df_空气指数.datetime[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 字符串和datetime的转换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2019-06-21 00:00:00+00:00\n",
       "1      2019-06-20 23:00:00+00:00\n",
       "2      2019-06-20 22:00:00+00:00\n",
       "3      2019-06-20 21:00:00+00:00\n",
       "4      2019-06-20 20:00:00+00:00\n",
       "                  ...           \n",
       "2063   2019-05-07 06:00:00+00:00\n",
       "2064   2019-05-07 04:00:00+00:00\n",
       "2065   2019-05-07 03:00:00+00:00\n",
       "2066   2019-05-07 02:00:00+00:00\n",
       "2067   2019-05-07 01:00:00+00:00\n",
       "Name: datetime, Length: 2068, dtype: datetime64[ns, UTC]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 日期的类型变为了datetime\n",
    "df_空气指数[\"datetime\"] = pd.to_datetime(df_空气指数[\"datetime\"])\n",
    "df_空气指数[\"datetime\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('2019-06-21 00:00:00+0000', tz='UTC'),\n",
       " Timestamp('2019-05-07 01:00:00+0000', tz='UTC'))"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看最大的一天和最小的一天\n",
    "# to_datetime 对单个字符串处理会得到Timestamp\n",
    "\n",
    "df_空气指数[\"datetime\"].max(),df_空气指数[\"datetime\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('44 days 23:00:00')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算这个表格计算了多少天\n",
    "# 时间增量\n",
    "\n",
    "df_空气指数[\"datetime\"].max() - df_空气指数[\"datetime\"].min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 在DataFrame仅包含测量周/月份/年/季度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>datetime</th>\n",
       "      <th>location</th>\n",
       "      <th>parameter</th>\n",
       "      <th>value</th>\n",
       "      <th>unit</th>\n",
       "      <th>weekofyear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-06-21 00:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>20.0</td>\n",
       "      <td>µg/m³</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-06-20 23:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>21.8</td>\n",
       "      <td>µg/m³</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-06-20 22:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>26.5</td>\n",
       "      <td>µg/m³</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-06-20 21:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>24.9</td>\n",
       "      <td>µg/m³</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-06-20 20:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>21.4</td>\n",
       "      <td>µg/m³</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0   city country                  datetime location parameter  \\\n",
       "0         NaN  Paris      FR 2019-06-21 00:00:00+00:00  FR04014       no2   \n",
       "1         NaN  Paris      FR 2019-06-20 23:00:00+00:00  FR04014       no2   \n",
       "2         NaN  Paris      FR 2019-06-20 22:00:00+00:00  FR04014       no2   \n",
       "3         NaN  Paris      FR 2019-06-20 21:00:00+00:00  FR04014       no2   \n",
       "4         NaN  Paris      FR 2019-06-20 20:00:00+00:00  FR04014       no2   \n",
       "\n",
       "   value   unit  weekofyear  \n",
       "0   20.0  µg/m³          25  \n",
       "1   21.8  µg/m³          25  \n",
       "2   26.5  µg/m³          25  \n",
       "3   24.9  µg/m³          25  \n",
       "4   21.4  µg/m³          25  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 周\n",
    "\n",
    "df_空气指数[\"weekofyear\"] = df_空气指数[\"datetime\"].dt.weekofyear\n",
    "df_空气指数.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>datetime</th>\n",
       "      <th>location</th>\n",
       "      <th>parameter</th>\n",
       "      <th>value</th>\n",
       "      <th>unit</th>\n",
       "      <th>weekofyear</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-06-21 00:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>20.0</td>\n",
       "      <td>µg/m³</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-06-20 23:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>21.8</td>\n",
       "      <td>µg/m³</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-06-20 22:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>26.5</td>\n",
       "      <td>µg/m³</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-06-20 21:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>24.9</td>\n",
       "      <td>µg/m³</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-06-20 20:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>21.4</td>\n",
       "      <td>µg/m³</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0   city country                  datetime location parameter  \\\n",
       "0         NaN  Paris      FR 2019-06-21 00:00:00+00:00  FR04014       no2   \n",
       "1         NaN  Paris      FR 2019-06-20 23:00:00+00:00  FR04014       no2   \n",
       "2         NaN  Paris      FR 2019-06-20 22:00:00+00:00  FR04014       no2   \n",
       "3         NaN  Paris      FR 2019-06-20 21:00:00+00:00  FR04014       no2   \n",
       "4         NaN  Paris      FR 2019-06-20 20:00:00+00:00  FR04014       no2   \n",
       "\n",
       "   value   unit  weekofyear  month  \n",
       "0   20.0  µg/m³          25      6  \n",
       "1   21.8  µg/m³          25      6  \n",
       "2   26.5  µg/m³          25      6  \n",
       "3   24.9  µg/m³          25      6  \n",
       "4   21.4  µg/m³          25      6  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 月份\n",
    "\n",
    "df_空气指数[\"month\"] = df_空气指数[\"datetime\"].dt.month\n",
    "df_空气指数.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>datetime</th>\n",
       "      <th>location</th>\n",
       "      <th>parameter</th>\n",
       "      <th>value</th>\n",
       "      <th>unit</th>\n",
       "      <th>weekofyear</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-06-21 00:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>20.0</td>\n",
       "      <td>µg/m³</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-06-20 23:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>21.8</td>\n",
       "      <td>µg/m³</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-06-20 22:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>26.5</td>\n",
       "      <td>µg/m³</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-06-20 21:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>24.9</td>\n",
       "      <td>µg/m³</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-06-20 20:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>21.4</td>\n",
       "      <td>µg/m³</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0   city country                  datetime location parameter  \\\n",
       "0         NaN  Paris      FR 2019-06-21 00:00:00+00:00  FR04014       no2   \n",
       "1         NaN  Paris      FR 2019-06-20 23:00:00+00:00  FR04014       no2   \n",
       "2         NaN  Paris      FR 2019-06-20 22:00:00+00:00  FR04014       no2   \n",
       "3         NaN  Paris      FR 2019-06-20 21:00:00+00:00  FR04014       no2   \n",
       "4         NaN  Paris      FR 2019-06-20 20:00:00+00:00  FR04014       no2   \n",
       "\n",
       "   value   unit  weekofyear  month  year  \n",
       "0   20.0  µg/m³          25      6  2019  \n",
       "1   21.8  µg/m³          25      6  2019  \n",
       "2   26.5  µg/m³          25      6  2019  \n",
       "3   24.9  µg/m³          25      6  2019  \n",
       "4   21.4  µg/m³          25      6  2019  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 年\n",
    "\n",
    "df_空气指数[\"year\"] = df_空气指数[\"datetime\"].dt.year\n",
    "df_空气指数.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>datetime</th>\n",
       "      <th>location</th>\n",
       "      <th>parameter</th>\n",
       "      <th>value</th>\n",
       "      <th>unit</th>\n",
       "      <th>weekofyear</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>quarter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-06-21 00:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>20.0</td>\n",
       "      <td>µg/m³</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-06-20 23:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>21.8</td>\n",
       "      <td>µg/m³</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-06-20 22:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>26.5</td>\n",
       "      <td>µg/m³</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-06-20 21:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>24.9</td>\n",
       "      <td>µg/m³</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-06-20 20:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>21.4</td>\n",
       "      <td>µg/m³</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0   city country                  datetime location parameter  \\\n",
       "0         NaN  Paris      FR 2019-06-21 00:00:00+00:00  FR04014       no2   \n",
       "1         NaN  Paris      FR 2019-06-20 23:00:00+00:00  FR04014       no2   \n",
       "2         NaN  Paris      FR 2019-06-20 22:00:00+00:00  FR04014       no2   \n",
       "3         NaN  Paris      FR 2019-06-20 21:00:00+00:00  FR04014       no2   \n",
       "4         NaN  Paris      FR 2019-06-20 20:00:00+00:00  FR04014       no2   \n",
       "\n",
       "   value   unit  weekofyear  month  year  quarter  \n",
       "0   20.0  µg/m³          25      6  2019        2  \n",
       "1   21.8  µg/m³          25      6  2019        2  \n",
       "2   26.5  µg/m³          25      6  2019        2  \n",
       "3   24.9  µg/m³          25      6  2019        2  \n",
       "4   21.4  µg/m³          25      6  2019        2  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 季度\n",
    "\n",
    "df_空气指数[\"quarter\"] = df_空气指数[\"datetime\"].dt.quarter\n",
    "df_空气指数.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 测量一周中每一天同一位置的 NO2平均值浓度是多少？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime  location          \n",
       "0         BETR801               27.875000\n",
       "          FR04014               24.856250\n",
       "          London Westminster    23.969697\n",
       "1         BETR801               22.214286\n",
       "          FR04014               30.999359\n",
       "          London Westminster    24.885714\n",
       "2         BETR801               21.125000\n",
       "          FR04014               29.165753\n",
       "          London Westminster    23.460432\n",
       "3         BETR801               27.500000\n",
       "          FR04014               28.600690\n",
       "          London Westminster    24.780142\n",
       "4         BETR801               28.400000\n",
       "          FR04014               31.617986\n",
       "          London Westminster    26.446809\n",
       "5         BETR801               33.500000\n",
       "          FR04014               25.266154\n",
       "          London Westminster    24.977612\n",
       "6         BETR801               21.896552\n",
       "          FR04014               23.274306\n",
       "          London Westminster    24.859155\n",
       "Name: value, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_空气指数.groupby([df_空气指数['datetime'].dt.weekday,'location'])['value'].mean()\n",
    "\n",
    "# 将44天分成7个组，计算每周的平均值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 可视化：一天中NO2浓度的变化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22cb26af128>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig, axs = plt.subplots(figsize=(12, 4))\n",
    "df_空气指数.groupby(df_空气指数[\"datetime\"].dt.hour)[\"value\"].mean().plot(kind='bar',rot=0,ax=axs)\n",
    "\n",
    "# 44天里有44个0时，那么就把这44个0时加起来取平均值，最后得出结果，但是会有空缺值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据重塑"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>location</th>\n",
       "      <th>BETR801</th>\n",
       "      <th>FR04014</th>\n",
       "      <th>London Westminster</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-05-07 01:00:00+00:00</th>\n",
       "      <td>50.5</td>\n",
       "      <td>25.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-07 02:00:00+00:00</th>\n",
       "      <td>45.0</td>\n",
       "      <td>27.7</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-07 03:00:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>50.4</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-07 04:00:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>61.9</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-07 05:00:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>72.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-20 20:00:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>21.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-20 21:00:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>24.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-20 22:00:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>26.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-20 23:00:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>21.8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-21 00:00:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1033 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "location                   BETR801  FR04014  London Westminster\n",
       "datetime                                                       \n",
       "2019-05-07 01:00:00+00:00     50.5     25.0                23.0\n",
       "2019-05-07 02:00:00+00:00     45.0     27.7                19.0\n",
       "2019-05-07 03:00:00+00:00      NaN     50.4                19.0\n",
       "2019-05-07 04:00:00+00:00      NaN     61.9                16.0\n",
       "2019-05-07 05:00:00+00:00      NaN     72.4                 NaN\n",
       "...                            ...      ...                 ...\n",
       "2019-06-20 20:00:00+00:00      NaN     21.4                 NaN\n",
       "2019-06-20 21:00:00+00:00      NaN     24.9                 NaN\n",
       "2019-06-20 22:00:00+00:00      NaN     26.5                 NaN\n",
       "2019-06-20 23:00:00+00:00      NaN     21.8                 NaN\n",
       "2019-06-21 00:00:00+00:00      NaN     20.0                 NaN\n",
       "\n",
       "[1033 rows x 3 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_2 = df_空气指数.pivot(index=\"datetime\", columns=\"location\", values=\"value\")\n",
    "no_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Int64Index([2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,\n",
       "             ...\n",
       "             2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019],\n",
       "            dtype='int64', name='datetime', length=1033),\n",
       " Int64Index([1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "             ...\n",
       "             3, 3, 3, 3, 3, 3, 3, 3, 3, 4],\n",
       "            dtype='int64', name='datetime', length=1033))"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_2.index.year,no_2.index.weekday"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 计算某个时间段的变化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "no_2[\"2019-05-13\":\"2019-05-14\"].plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 时间采样\n",
    "* 将时间序列重新采样到另一个频率\n",
    "* 会产生偏移值\n",
    "* resample():改变数据的时间频率，例如把小时数据变成分钟级别，将天数变成月份\n",
    "    * 类似于groupby()，但是比groupby()强大\n",
    "    * 可以改变原始时间的起始点，给它一个偏移值，然后按照某一个频率去计算时间\n",
    "    * 兼顾了groupby()+agg(),有偏移值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>location</th>\n",
       "      <th>BETR801</th>\n",
       "      <th>FR04014</th>\n",
       "      <th>London Westminster</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-05-13 00:00:00+00:00</th>\n",
       "      <td>22.535714</td>\n",
       "      <td>26.554878</td>\n",
       "      <td>30.753165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-20 00:00:00+00:00</th>\n",
       "      <td>23.980769</td>\n",
       "      <td>27.114970</td>\n",
       "      <td>31.035928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-27 00:00:00+00:00</th>\n",
       "      <td>37.642857</td>\n",
       "      <td>32.688485</td>\n",
       "      <td>28.718563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-03 00:00:00+00:00</th>\n",
       "      <td>20.200000</td>\n",
       "      <td>25.180000</td>\n",
       "      <td>20.850299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-10 00:00:00+00:00</th>\n",
       "      <td>14.500000</td>\n",
       "      <td>25.224503</td>\n",
       "      <td>17.699387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-17 00:00:00+00:00</th>\n",
       "      <td>34.178571</td>\n",
       "      <td>28.922424</td>\n",
       "      <td>19.074830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-24 00:00:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>31.070370</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "location                     BETR801    FR04014  London Westminster\n",
       "datetime                                                           \n",
       "2019-05-13 00:00:00+00:00  22.535714  26.554878           30.753165\n",
       "2019-05-20 00:00:00+00:00  23.980769  27.114970           31.035928\n",
       "2019-05-27 00:00:00+00:00  37.642857  32.688485           28.718563\n",
       "2019-06-03 00:00:00+00:00  20.200000  25.180000           20.850299\n",
       "2019-06-10 00:00:00+00:00  14.500000  25.224503           17.699387\n",
       "2019-06-17 00:00:00+00:00  34.178571  28.922424           19.074830\n",
       "2019-06-24 00:00:00+00:00        NaN  31.070370                 NaN"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_max = no_2.resample(\"W-MON\").mean()   # 这里 monday 就是一个偏移值\n",
    "# W 代表每一周，可翻倍叠加，如3W，这时表示每三周\n",
    "monthly_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "no_2.resample(\"W-MON\").mean().plot(style=\"-o\", figsize=(10, 5));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "no_2.resample(\"D\").mean().plot(style=\"-o\", figsize=(10, 5));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 独角兽"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
